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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from path_explain import utils\n",
    "utils.set_up_environment(visible_devices='1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow_datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import scipy\n",
    "from bert_explainer import BertExplainerTF\n",
    "from path_explain.path_explainer_tf import PathExplainerTF\n",
    "from transformers import *\n",
    "from plot.text import text_plot\n",
    "import transformers\n",
    "from tqdm import tqdm\n",
    "from functools import reduce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data and Model Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = 'sts-b'\n",
    "num_labels = len(glue_processors[task]().get_labels())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = BertConfig.from_pretrained('.', num_labels=num_labels)\n",
    "tokenizer = BertTokenizer.from_pretrained('bert-base-cased')\n",
    "model = TFBertForSequenceClassification.from_pretrained('.', config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:absl:Overwrite dataset info from restored data version.\n",
      "INFO:absl:Reusing dataset glue (/homes/gws/psturm/tensorflow_datasets/glue/stsb/0.0.2)\n",
      "INFO:absl:Constructing tf.data.Dataset for split None, from /homes/gws/psturm/tensorflow_datasets/glue/stsb/0.0.2\n"
     ]
    }
   ],
   "source": [
    "data, info = tensorflow_datasets.load('glue/stsb', with_info=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task=task)\n",
    "valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, max_length=128, task=task)\n",
    "valid_dataset = valid_dataset.batch(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_pred = model.predict(valid_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_input = []\n",
    "valid_labels = []\n",
    "for batch in valid_dataset:\n",
    "    valid_input.append(batch[0])\n",
    "    valid_labels.append(batch[1].numpy())\n",
    "valid_labels_np = np.concatenate(valid_labels, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_valid_ids = [batch['input_ids'] for batch in valid_input]\n",
    "concat_valid_ids = tf.concat(concat_valid_ids, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation MSE: 0.5685 (variance explained: 0.7440)\n"
     ]
    }
   ],
   "source": [
    "valid_mse = np.mean(np.square(valid_pred[:, 0] - valid_labels_np))\n",
    "\n",
    "print('Validation MSE: {:.4f} (variance explained: {:.4f})'.format(valid_mse,\n",
    "                                                                   1.0 - valid_mse / np.var(valid_labels_np)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_df = pd.DataFrame({\n",
    "    'Predicted Label': valid_pred[:, 0],\n",
    "    'True Label': valid_labels_np\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(label_df).transform_density(\n",
    "    'Predicted Label',\n",
    "    as_=['Predicted Label', 'density'],\n",
    "    extent=[-0.5, 5.5],\n",
    "    groupby=['True Label']\n",
    ").mark_area(orient='horizontal').encode(\n",
    "    y='Predicted Label:Q',\n",
    "    color='True Label:N',\n",
    "    x=alt.X(\n",
    "        'density:Q',\n",
    "        stack='center',\n",
    "        impute=None,\n",
    "        title=None,\n",
    "        axis=alt.Axis(labels=False, values=[0],grid=False, ticks=True),\n",
    "    ),\n",
    "    column=alt.Column(\n",
    "        'True Label:N',\n",
    "        header=alt.Header(\n",
    "            titleOrient='bottom',\n",
    "            labelOrient='bottom',\n",
    "            labelPadding=0,\n",
    "        ),\n",
    "    )\n",
    ").properties(\n",
    "    width=100\n",
    ").configure_facet(\n",
    "    spacing=0\n",
    ").configure_view(\n",
    "    stroke=None\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_pred = valid_pred[:16]\n",
    "batch_labels = valid_labels[0]\n",
    "batch_input = valid_input[0]\n",
    "batch_ids = batch_input['input_ids']\n",
    "batch_baseline = concat_valid_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "explainer = BertExplainerTF(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 16/16 [06:38<00:00, 24.93s/it]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    attributions = np.load('attributions.npy')\n",
    "except FileNotFoundError as e:\n",
    "    attributions = explainer.attributions(inputs=batch_ids,\n",
    "                                          baseline=batch_baseline,\n",
    "                                          batch_size=30,\n",
    "                                          num_samples=1000,\n",
    "                                          use_expectation=True,\n",
    "                                          output_indices=0,\n",
    "                                          verbose=True)\n",
    "    np.save('attributions.npy', attributions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_completeness(index):\n",
    "    current_input = {\n",
    "        'input_ids': batch_input['input_ids'][index:index+1],\n",
    "        'attention_mask': batch_input['attention_mask'][index:index+1],\n",
    "        'token_type_ids': batch_input['token_type_ids'][index:index+1],\n",
    "    }\n",
    "\n",
    "    current_baseline = {\n",
    "        'input_ids': np.zeros((1, 128)).astype(int),\n",
    "        'attention_mask': batch_input['attention_mask'][index:index+1],\n",
    "        'token_type_ids': batch_input['token_type_ids'][index:index+1],\n",
    "    }\n",
    "\n",
    "    current_output = model(current_input)[0]\n",
    "    baseline_output = model(current_baseline)[0]\n",
    "    output_difference = current_output - baseline_output\n",
    "    output_difference = output_difference[0, 0]\n",
    "    sum_attr = np.sum(attributions[index, :])\n",
    "\n",
    "    encoded_sentence = batch_input['input_ids'].numpy()[index]\n",
    "    encoded_sentence = encoded_sentence[encoded_sentence != 0]\n",
    "    decoded_sentence = tokenizer.decode(encoded_sentence)\n",
    "\n",
    "    sentences = decoded_sentence.split('[SEP]')\n",
    "    first_sentence = sentences[0][6:]\n",
    "    second_sentence = sentences[1][1:]\n",
    "    label = batch_labels[index]\n",
    "    print('1) ' + first_sentence)\n",
    "    print('2) ' + second_sentence)\n",
    "    print('True similarity: {} - Predicted similarity: {:.4f})'.format(label, current_output[0, 0]))\n",
    "    print('Output difference:\\t{:.4f} ({:.4f} - {:.4})'.format(output_difference,\n",
    "                                                               current_output[0, 0],\n",
    "                                                               baseline_output[0, 0]))\n",
    "    print('Sum of attributions:\\t{:.4f}'.format(sum_attr))\n",
    "    print('-------------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1) Representatives for Puretunes could not immediately be reached for comment Wednesday. \n",
      "2) Puretunes representatives could not be located Thursday to comment on the suit. \n",
      "True similarity: 3 - Predicted similarity: 3.7143)\n",
      "Output difference:\t1.4649 (3.7143 - 2.249)\n",
      "Sum of attributions:\t-0.0377\n",
      "-------------------------\n",
      "1) North Korea Nuclear Test Sparks Worry \n",
      "2) North Korea nuclear test \n",
      "True similarity: 2 - Predicted similarity: 2.8583)\n",
      "Output difference:\t0.9098 (2.8583 - 1.949)\n",
      "Sum of attributions:\t-0.7320\n",
      "-------------------------\n",
      "1) A man in red swim trunks playing volleyball. \n",
      "2) A man and woman sitting on a motorcycle. \n",
      "True similarity: 0 - Predicted similarity: -0.0216)\n",
      "Output difference:\t-2.1308 (-0.0216 - 2.109)\n",
      "Sum of attributions:\t0.0436\n",
      "-------------------------\n",
      "1) A group of people are at a convention waving American flags. \n",
      "2) A crowd of people at an outdoor event \n",
      "True similarity: 2 - Predicted similarity: 1.6829)\n",
      "Output difference:\t-0.4440 (1.6829 - 2.127)\n",
      "Sum of attributions:\t-0.1771\n",
      "-------------------------\n",
      "1) A dog and a red ball in the air. \n",
      "2) A dog digs a hole in a yard. \n",
      "True similarity: 1 - Predicted similarity: 1.0293)\n",
      "Output difference:\t-1.1124 (1.0293 - 2.142)\n",
      "Sum of attributions:\t-0.1519\n",
      "-------------------------\n",
      "1) As I understand it, nothing that can be changed, or broken down into smaller parts is inherently real. \n",
      "2) Our interactions are merely depends on where we put our perception. \n",
      "True similarity: 0 - Predicted similarity: 0.6312)\n",
      "Output difference:\t-1.5876 (0.6312 - 2.219)\n",
      "Sum of attributions:\t-0.1281\n",
      "-------------------------\n",
      "1) The world's largest software company said it recognized the difficulty the multiple patches posed for companies, and set out to make it easier for them to apply the updates. \n",
      "2) The world's largest software company said it recognized the difficulty the multiple patches posed for companies trying to apply them. \n",
      "True similarity: 3 - Predicted similarity: 3.3955)\n",
      "Output difference:\t1.0406 (3.3955 - 2.355)\n",
      "Sum of attributions:\t-0.2608\n",
      "-------------------------\n",
      "1) In the past it has grown almost like a weed in our pots on our deck here in North Texas. \n",
      "2) Additional to the other answers, basil famously doesn't like to be damp overnight. \n",
      "True similarity: 0 - Predicted similarity: 0.1606)\n",
      "Output difference:\t-2.1361 (0.1606 - 2.297)\n",
      "Sum of attributions:\t-0.0478\n",
      "-------------------------\n",
      "1) Syrians trade blame over blasts \n",
      "2) Israel braces for Arab protests \n",
      "True similarity: 0 - Predicted similarity: 0.2130)\n",
      "Output difference:\t-1.8292 (0.2130 - 2.042)\n",
      "Sum of attributions:\t-0.1014\n",
      "-------------------------\n",
      "1) A black and wood colored dining room set in a small apartment. \n",
      "2) Black and brown table in living room setting. \n",
      "True similarity: 3 - Predicted similarity: 2.5046)\n",
      "Output difference:\t0.3359 (2.5046 - 2.169)\n",
      "Sum of attributions:\t0.1622\n",
      "-------------------------\n",
      "1) The part of economics that is most like physics is called microeconomics. \n",
      "2) Side note first : I don't think that physicists always agree on their theories and predictions. \n",
      "True similarity: 0 - Predicted similarity: 0.7233)\n",
      "Output difference:\t-1.5930 (0.7233 - 2.316)\n",
      "Sum of attributions:\t-0.3912\n",
      "-------------------------\n",
      "1) UN Chemical Weapons Experts to Visit Syria \n",
      "2) US Says Chemical Weapons Used in Syria \n",
      "True similarity: 2 - Predicted similarity: 2.7167)\n",
      "Output difference:\t0.6032 (2.7167 - 2.114)\n",
      "Sum of attributions:\t-0.5374\n",
      "-------------------------\n",
      "1) Back in the day, \" cable \" was used to describe communications sent abroad. \n",
      "2) The word \" cable \" has its origins in the days of the telegraph. \n",
      "True similarity: 3 - Predicted similarity: 3.2380)\n",
      "Output difference:\t0.9731 (3.2380 - 2.265)\n",
      "Sum of attributions:\t0.5070\n",
      "-------------------------\n",
      "1) Floods leave six dead in Philippines \n",
      "2) Heavy rains leave 18 dead in Philippines \n",
      "True similarity: 2 - Predicted similarity: 3.9809)\n",
      "Output difference:\t1.8968 (3.9809 - 2.084)\n",
      "Sum of attributions:\t0.0047\n",
      "-------------------------\n",
      "1) A man plays the guitar. \n",
      "2) The man sang and played his guitar. \n",
      "True similarity: 3 - Predicted similarity: 1.9956)\n",
      "Output difference:\t-0.1149 (1.9956 - 2.11)\n",
      "Sum of attributions:\t-0.0359\n",
      "-------------------------\n",
      "1) Senators can return to playoffs with victory \n",
      "2) Messi scores 4 to send Guardiola off with victory \n",
      "True similarity: 1 - Predicted similarity: 0.0975)\n",
      "Output difference:\t-2.0709 (0.0975 - 2.168)\n",
      "Sum of attributions:\t-0.1865\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "for i in range(16):\n",
    "    check_completeness(i)"
   ]
  }
 ],
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